What is the Difference Between Pre-translation via AI Fine-tuned model and Translation Memory?

Hello, I would like to know, what is the difference between pre-translating with Translation Memory and pre-translating with GPT Fine-tuned model?
For example, let’s say I trained AI with Translation Memory and then I pre-translate using AI.
I could also pre-translate directly with Translation Memory.
What is the difference between these?


Translation memory serves as a translation database. In short, it’s a storage of all your translations.

AI localization works differently. With the fine-tuning feature, you can pre-train the LLM model with your translation assets, such as TM and glossary.

So, in the long run, your LLM model will be well-trained and will be able to provide the most personalized results.

More information can be found here:

We also recommend checking our course Unlocking AI in Translation: Master Context, Fine-Tuning, and Data Security for Superior Results